Overview

Dataset statistics

Number of variables29
Number of observations3488
Missing cells228
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory766.7 KiB
Average record size in memory225.1 B

Variable types

Numeric17
Categorical11
DateTime1

Alerts

country has constant value "Australia" Constant
job_title has a high cardinality: 196 distinct values High cardinality
address has a high cardinality: 3486 distinct values High cardinality
df_index is highly correlated with customer_idHigh correlation
customer_id is highly correlated with df_indexHigh correlation
past_3_years_bike_related_purchases is highly correlated with Frequency and 5 other fieldsHigh correlation
tenure is highly correlated with age and 1 other fieldsHigh correlation
age is highly correlated with tenure and 1 other fieldsHigh correlation
postcode is highly correlated with state and 1 other fieldsHigh correlation
property_valuation is highly correlated with postcodeHigh correlation
Frequency is highly correlated with past_3_years_bike_related_purchases and 5 other fieldsHigh correlation
profit is highly correlated with M_rankHigh correlation
Recency is highly correlated with R_rank and 1 other fieldsHigh correlation
R_rank is highly correlated with Recency and 3 other fieldsHigh correlation
F_rank is highly correlated with past_3_years_bike_related_purchases and 5 other fieldsHigh correlation
M_rank is highly correlated with profitHigh correlation
R_rank_norm is highly correlated with Recency and 3 other fieldsHigh correlation
F_rank_norm is highly correlated with past_3_years_bike_related_purchases and 5 other fieldsHigh correlation
M_rank_norm is highly correlated with past_3_years_bike_related_purchases and 5 other fieldsHigh correlation
RFM_Score is highly correlated with past_3_years_bike_related_purchases and 7 other fieldsHigh correlation
gender is highly correlated with job_industry_category and 1 other fieldsHigh correlation
job_industry_category is highly correlated with gender and 1 other fieldsHigh correlation
wealth_segment is highly correlated with countryHigh correlation
deceased_indicator is highly correlated with countryHigh correlation
owns_car is highly correlated with countryHigh correlation
age_group is highly correlated with gender and 3 other fieldsHigh correlation
state is highly correlated with postcodeHigh correlation
country is highly correlated with age_group and 7 other fieldsHigh correlation
Customer_segment is highly correlated with past_3_years_bike_related_purchases and 7 other fieldsHigh correlation
DOB has 76 (2.2%) missing values Missing
tenure has 76 (2.2%) missing values Missing
age has 76 (2.2%) missing values Missing
df_index is uniformly distributed Uniform
customer_id is uniformly distributed Uniform
address is uniformly distributed Uniform
M_rank is uniformly distributed Uniform
df_index has unique values Unique
customer_id has unique values Unique
Recency has 47 (1.3%) zeros Zeros

Reproduction

Analysis started2022-12-15 11:22:04.126478
Analysis finished2022-12-15 11:23:05.721862
Duration1 minute and 1.6 second
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct3488
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1744.491686
Minimum0
Maximum3488
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size27.4 KiB
2022-12-15T11:23:05.850787image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile175.35
Q1872.75
median1744.5
Q32616.25
95-th percentile3313.65
Maximum3488
Range3488
Interquartile range (IQR)1743.5

Descriptive statistics

Standard deviation1007.057484
Coefficient of variation (CV)0.5772784656
Kurtosis-1.199934525
Mean1744.491686
Median Absolute Deviation (MAD)872
Skewness-4.837494574 × 10-5
Sum6084787
Variance1014164.775
MonotonicityStrictly increasing
2022-12-15T11:23:07.027320image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
23301
 
< 0.1%
23191
 
< 0.1%
23201
 
< 0.1%
23211
 
< 0.1%
23221
 
< 0.1%
23231
 
< 0.1%
23241
 
< 0.1%
23251
 
< 0.1%
23261
 
< 0.1%
Other values (3478)3478
99.7%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
34881
< 0.1%
34871
< 0.1%
34861
< 0.1%
34851
< 0.1%
34841
< 0.1%
34831
< 0.1%
34821
< 0.1%
34811
< 0.1%
34801
< 0.1%
34791
< 0.1%

customer_id
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct3488
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1752.398222
Minimum1
Maximum3500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.4 KiB
2022-12-15T11:23:07.236555image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile180.35
Q1879.75
median1752.5
Q32625.25
95-th percentile3325.65
Maximum3500
Range3499
Interquartile range (IQR)1745.5

Descriptive statistics

Standard deviation1009.114046
Coefficient of variation (CV)0.5758474487
Kurtosis-1.199495742
Mean1752.398222
Median Absolute Deviation (MAD)873
Skewness-1.87091051 × 10-5
Sum6112365
Variance1018311.157
MonotonicityStrictly increasing
2022-12-15T11:23:07.434942image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
< 0.1%
23391
 
< 0.1%
23281
 
< 0.1%
23291
 
< 0.1%
23301
 
< 0.1%
23311
 
< 0.1%
23321
 
< 0.1%
23331
 
< 0.1%
23341
 
< 0.1%
23351
 
< 0.1%
Other values (3478)3478
99.7%
ValueCountFrequency (%)
11
< 0.1%
21
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
111
< 0.1%
121
< 0.1%
ValueCountFrequency (%)
35001
< 0.1%
34991
< 0.1%
34981
< 0.1%
34971
< 0.1%
34961
< 0.1%
34951
< 0.1%
34941
< 0.1%
34931
< 0.1%
34921
< 0.1%
34911
< 0.1%

gender
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
Female
1758 
Male
1654 
Unknown
 
76

Length

Max length7
Median length6
Mean length5.073394495
Min length4

Characters and Unicode

Total characters17696
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowMale
3rd rowMale
4th rowFemale
5th rowMale

Common Values

ValueCountFrequency (%)
Female1758
50.4%
Male1654
47.4%
Unknown76
 
2.2%

Length

2022-12-15T11:23:07.640993image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-12-15T11:23:07.829271image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
female1758
50.4%
male1654
47.4%
unknown76
 
2.2%

Most occurring characters

ValueCountFrequency (%)
e5170
29.2%
a3412
19.3%
l3412
19.3%
F1758
 
9.9%
m1758
 
9.9%
M1654
 
9.3%
n228
 
1.3%
U76
 
0.4%
k76
 
0.4%
o76
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter14208
80.3%
Uppercase Letter3488
 
19.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e5170
36.4%
a3412
24.0%
l3412
24.0%
m1758
 
12.4%
n228
 
1.6%
k76
 
0.5%
o76
 
0.5%
w76
 
0.5%
Uppercase Letter
ValueCountFrequency (%)
F1758
50.4%
M1654
47.4%
U76
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
Latin17696
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e5170
29.2%
a3412
19.3%
l3412
19.3%
F1758
 
9.9%
m1758
 
9.9%
M1654
 
9.3%
n228
 
1.3%
U76
 
0.4%
k76
 
0.4%
o76
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII17696
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e5170
29.2%
a3412
19.3%
l3412
19.3%
F1758
 
9.9%
m1758
 
9.9%
M1654
 
9.3%
n228
 
1.3%
U76
 
0.4%
k76
 
0.4%
o76
 
0.4%

past_3_years_bike_related_purchases
Real number (ℝ≥0)

HIGH CORRELATION

Distinct100
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.79300459
Minimum0
Maximum99
Zeros34
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size27.4 KiB
2022-12-15T11:23:08.013504image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q124
median48
Q373
95-th percentile95
Maximum99
Range99
Interquartile range (IQR)49

Descriptive statistics

Standard deviation28.61093779
Coefficient of variation (CV)0.5863737647
Kurtosis-1.176513967
Mean48.79300459
Median Absolute Deviation (MAD)25
Skewness0.05879815324
Sum170190
Variance818.5857612
MonotonicityNot monotonic
2022-12-15T11:23:08.219032image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6750
 
1.4%
1650
 
1.4%
2049
 
1.4%
5347
 
1.3%
8046
 
1.3%
244
 
1.3%
4844
 
1.3%
9844
 
1.3%
3343
 
1.2%
8342
 
1.2%
Other values (90)3029
86.8%
ValueCountFrequency (%)
034
1.0%
128
0.8%
244
1.3%
324
0.7%
432
0.9%
527
0.8%
641
1.2%
732
0.9%
822
0.6%
936
1.0%
ValueCountFrequency (%)
9936
1.0%
9844
1.3%
9740
1.1%
9642
1.2%
9525
0.7%
9433
0.9%
9339
1.1%
9221
0.6%
9128
0.8%
9034
1.0%

DOB
Date

MISSING

Distinct3046
Distinct (%)89.3%
Missing76
Missing (%)2.2%
Memory size27.4 KiB
Minimum1931-10-23 00:00:00
Maximum2002-03-11 00:00:00
2022-12-15T11:23:08.434196image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:23:08.636009image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

job_title
Categorical

HIGH CARDINALITY

Distinct196
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size27.4 KiB
Unknown
421 
Business Systems Development Analyst
 
40
Social Worker
 
38
Tax Accountant
 
37
Executive Secretary
 
36
Other values (191)
2916 

Length

Max length36
Median length26
Mean length16.87614679
Min length5

Characters and Unicode

Total characters58864
Distinct characters48
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowExecutive Secretary
2nd rowAdministrative Officer
3rd rowUnknown
4th rowSenior Editor
5th rowUnknown

Common Values

ValueCountFrequency (%)
Unknown421
 
12.1%
Business Systems Development Analyst40
 
1.1%
Social Worker38
 
1.1%
Tax Accountant37
 
1.1%
Executive Secretary36
 
1.0%
Internal Auditor36
 
1.0%
Legal Assistant36
 
1.0%
Associate Professor35
 
1.0%
General Manager35
 
1.0%
Structural Engineer34
 
1.0%
Other values (186)2740
78.6%

Length

2022-12-15T11:23:08.878776image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
engineer429
 
5.6%
unknown421
 
5.5%
assistant295
 
3.9%
manager241
 
3.2%
analyst235
 
3.1%
iv198
 
2.6%
iii184
 
2.4%
i183
 
2.4%
ii178
 
2.3%
systems156
 
2.0%
Other values (118)5110
67.0%

Most occurring characters

ValueCountFrequency (%)
n5500
 
9.3%
e5471
 
9.3%
t4327
 
7.4%
a4148
 
7.0%
4142
 
7.0%
i4061
 
6.9%
r3644
 
6.2%
s3424
 
5.8%
o2977
 
5.1%
c2450
 
4.2%
Other values (38)18720
31.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter46149
78.4%
Uppercase Letter8542
 
14.5%
Space Separator4142
 
7.0%
Other Punctuation31
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n5500
11.9%
e5471
11.9%
t4327
9.4%
a4148
9.0%
i4061
8.8%
r3644
7.9%
s3424
7.4%
o2977
 
6.5%
c2450
 
5.3%
l1734
 
3.8%
Other values (14)8413
18.2%
Uppercase Letter
ValueCountFrequency (%)
I1378
16.1%
A1259
14.7%
S990
11.6%
E636
 
7.4%
P606
 
7.1%
C443
 
5.2%
U421
 
4.9%
D419
 
4.9%
M389
 
4.6%
V341
 
4.0%
Other values (12)1660
19.4%
Space Separator
ValueCountFrequency (%)
4142
100.0%
Other Punctuation
ValueCountFrequency (%)
/31
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin54691
92.9%
Common4173
 
7.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
n5500
 
10.1%
e5471
 
10.0%
t4327
 
7.9%
a4148
 
7.6%
i4061
 
7.4%
r3644
 
6.7%
s3424
 
6.3%
o2977
 
5.4%
c2450
 
4.5%
l1734
 
3.2%
Other values (36)16955
31.0%
Common
ValueCountFrequency (%)
4142
99.3%
/31
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII58864
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n5500
 
9.3%
e5471
 
9.3%
t4327
 
7.4%
a4148
 
7.0%
4142
 
7.0%
i4061
 
6.9%
r3644
 
6.2%
s3424
 
5.8%
o2977
 
5.1%
c2450
 
4.2%
Other values (38)18720
31.8%

job_industry_category
Categorical

HIGH CORRELATION

Distinct10
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size27.4 KiB
Manufacturing
703 
Financial Services
686 
Unknown
560 
Health
532 
Retail
304 
Other values (5)
703 

Length

Max length18
Median length11
Mean length10.45584862
Min length2

Characters and Unicode

Total characters36470
Distinct characters32
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHealth
2nd rowFinancial Services
3rd rowIT
4th rowUnknown
5th rowRetail

Common Values

ValueCountFrequency (%)
Manufacturing703
20.2%
Financial Services686
19.7%
Unknown560
16.1%
Health532
15.3%
Retail304
8.7%
Property230
 
6.6%
IT187
 
5.4%
Entertainment123
 
3.5%
Argiculture100
 
2.9%
Telecommunications63
 
1.8%

Length

2022-12-15T11:23:09.037674image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-12-15T11:23:09.284159image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
manufacturing703
16.8%
financial686
16.4%
services686
16.4%
unknown560
13.4%
health532
12.7%
retail304
7.3%
property230
 
5.5%
it187
 
4.5%
entertainment123
 
2.9%
argiculture100
 
2.4%

Most occurring characters

ValueCountFrequency (%)
n4953
13.6%
a3800
 
10.4%
i3414
 
9.4%
e2910
 
8.0%
c2301
 
6.3%
t2301
 
6.3%
r2172
 
6.0%
l1685
 
4.6%
u1669
 
4.6%
o916
 
2.5%
Other values (22)10349
28.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter31423
86.2%
Uppercase Letter4361
 
12.0%
Space Separator686
 
1.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n4953
15.8%
a3800
12.1%
i3414
10.9%
e2910
9.3%
c2301
7.3%
t2301
7.3%
r2172
6.9%
l1685
 
5.4%
u1669
 
5.3%
o916
 
2.9%
Other values (10)5302
16.9%
Uppercase Letter
ValueCountFrequency (%)
M703
16.1%
S686
15.7%
F686
15.7%
U560
12.8%
H532
12.2%
R304
7.0%
T250
 
5.7%
P230
 
5.3%
I187
 
4.3%
E123
 
2.8%
Space Separator
ValueCountFrequency (%)
686
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin35784
98.1%
Common686
 
1.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
n4953
13.8%
a3800
 
10.6%
i3414
 
9.5%
e2910
 
8.1%
c2301
 
6.4%
t2301
 
6.4%
r2172
 
6.1%
l1685
 
4.7%
u1669
 
4.7%
o916
 
2.6%
Other values (21)9663
27.0%
Common
ValueCountFrequency (%)
686
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII36470
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n4953
13.6%
a3800
 
10.4%
i3414
 
9.4%
e2910
 
8.0%
c2301
 
6.3%
t2301
 
6.3%
r2172
 
6.0%
l1685
 
4.6%
u1669
 
4.6%
o916
 
2.5%
Other values (22)10349
28.4%

wealth_segment
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.4 KiB
Mass Customer
1744 
High Net Worth
894 
Affluent Customer
850 

Length

Max length17
Median length15.5
Mean length14.23107798
Min length13

Characters and Unicode

Total characters49638
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMass Customer
2nd rowMass Customer
3rd rowMass Customer
4th rowAffluent Customer
5th rowHigh Net Worth

Common Values

ValueCountFrequency (%)
Mass Customer1744
50.0%
High Net Worth894
25.6%
Affluent Customer850
24.4%

Length

2022-12-15T11:23:09.496773image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-12-15T11:23:09.684517image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
customer2594
33.0%
mass1744
22.2%
high894
 
11.4%
net894
 
11.4%
worth894
 
11.4%
affluent850
 
10.8%

Most occurring characters

ValueCountFrequency (%)
s6082
12.3%
t5232
10.5%
4382
 
8.8%
e4338
 
8.7%
r3488
 
7.0%
o3488
 
7.0%
u3444
 
6.9%
C2594
 
5.2%
m2594
 
5.2%
h1788
 
3.6%
Other values (11)12208
24.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter37386
75.3%
Uppercase Letter7870
 
15.9%
Space Separator4382
 
8.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s6082
16.3%
t5232
14.0%
e4338
11.6%
r3488
9.3%
o3488
9.3%
u3444
9.2%
m2594
6.9%
h1788
 
4.8%
a1744
 
4.7%
f1700
 
4.5%
Other values (4)3488
9.3%
Uppercase Letter
ValueCountFrequency (%)
C2594
33.0%
M1744
22.2%
H894
 
11.4%
N894
 
11.4%
W894
 
11.4%
A850
 
10.8%
Space Separator
ValueCountFrequency (%)
4382
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin45256
91.2%
Common4382
 
8.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
s6082
13.4%
t5232
11.6%
e4338
9.6%
r3488
 
7.7%
o3488
 
7.7%
u3444
 
7.6%
C2594
 
5.7%
m2594
 
5.7%
h1788
 
4.0%
M1744
 
3.9%
Other values (10)10464
23.1%
Common
ValueCountFrequency (%)
4382
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII49638
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s6082
12.3%
t5232
10.5%
4382
 
8.8%
e4338
 
8.7%
r3488
 
7.0%
o3488
 
7.0%
u3444
 
6.9%
C2594
 
5.2%
m2594
 
5.2%
h1788
 
3.6%
Other values (11)12208
24.6%

deceased_indicator
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.4 KiB
0
3487 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3488
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
03487
> 99.9%
11
 
< 0.1%

Length

2022-12-15T11:23:09.821178image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-12-15T11:23:09.996143image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
03487
> 99.9%
11
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
03487
> 99.9%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3488
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03487
> 99.9%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common3488
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03487
> 99.9%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII3488
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03487
> 99.9%
11
 
< 0.1%

owns_car
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.4 KiB
1
1767 
0
1721 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3488
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
11767
50.7%
01721
49.3%

Length

2022-12-15T11:23:10.117049image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-12-15T11:23:10.289337image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
11767
50.7%
01721
49.3%

Most occurring characters

ValueCountFrequency (%)
11767
50.7%
01721
49.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3488
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
11767
50.7%
01721
49.3%

Most occurring scripts

ValueCountFrequency (%)
Common3488
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
11767
50.7%
01721
49.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII3488
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11767
50.7%
01721
49.3%

tenure
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct22
Distinct (%)0.6%
Missing76
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean10.67848769
Minimum1
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.4 KiB
2022-12-15T11:23:10.414630image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q16
median11
Q315
95-th percentile20
Maximum22
Range21
Interquartile range (IQR)9

Descriptive statistics

Standard deviation5.673060209
Coefficient of variation (CV)0.5312606404
Kurtosis-1.064779769
Mean10.67848769
Median Absolute Deviation (MAD)5
Skewness0.05121290579
Sum36435
Variance32.18361214
MonotonicityNot monotonic
2022-12-15T11:23:10.586342image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
7203
 
5.8%
5195
 
5.6%
11193
 
5.5%
16188
 
5.4%
8184
 
5.3%
14183
 
5.2%
12178
 
5.1%
10177
 
5.1%
18175
 
5.0%
9174
 
5.0%
Other values (12)1562
44.8%
ValueCountFrequency (%)
1148
4.2%
2130
3.7%
3142
4.1%
4168
4.8%
5195
5.6%
6162
4.6%
7203
5.8%
8184
5.3%
9174
5.0%
10177
5.1%
ValueCountFrequency (%)
2248
 
1.4%
2147
 
1.3%
2086
2.5%
19140
4.0%
18175
5.0%
17166
4.8%
16188
5.4%
15152
4.4%
14183
5.2%
13173
5.0%

age
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct55
Distinct (%)1.6%
Missing76
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean44.82473623
Minimum20
Maximum91
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.4 KiB
2022-12-15T11:23:10.781621image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile24
Q135
median45
Q354
95-th percentile66
Maximum91
Range71
Interquartile range (IQR)19

Descriptive statistics

Standard deviation12.57954103
Coefficient of variation (CV)0.2806383728
Kurtosis-0.7676827226
Mean44.82473623
Median Absolute Deviation (MAD)9
Skewness0.01847513031
Sum152942
Variance158.2448526
MonotonicityNot monotonic
2022-12-15T11:23:10.985510image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
44187
 
5.4%
45181
 
5.2%
48135
 
3.9%
46134
 
3.8%
42114
 
3.3%
49108
 
3.1%
43107
 
3.1%
3699
 
2.8%
4797
 
2.8%
6387
 
2.5%
Other values (45)2163
62.0%
ValueCountFrequency (%)
206
 
0.2%
2129
 
0.8%
2238
1.1%
2352
1.5%
2471
2.0%
2563
1.8%
2657
1.6%
2786
2.5%
2865
1.9%
2952
1.5%
ValueCountFrequency (%)
911
 
< 0.1%
871
 
< 0.1%
821
 
< 0.1%
791
 
< 0.1%
781
 
< 0.1%
6918
 
0.5%
6849
1.4%
6750
1.4%
6654
1.5%
6558
1.7%

age_group
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.4 KiB
Gen X (40-54 years)
1465 
Millennials (25-39 years)
917 
Baby Boomers (55-74 years)
829 
Gen Z (10-24 years)
196 
Interwar
 
81

Length

Max length26
Median length25
Mean length21.98566514
Min length8

Characters and Unicode

Total characters76686
Distinct characters31
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBaby Boomers (55-74 years)
2nd rowGen X (40-54 years)
3rd rowBaby Boomers (55-74 years)
4th rowGen X (40-54 years)
5th rowBaby Boomers (55-74 years)

Common Values

ValueCountFrequency (%)
Gen X (40-54 years)1465
42.0%
Millennials (25-39 years)917
26.3%
Baby Boomers (55-74 years)829
23.8%
Gen Z (10-24 years)196
 
5.6%
Interwar81
 
2.3%

Length

2022-12-15T11:23:11.185630image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-12-15T11:23:11.378134image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
years3407
26.6%
gen1661
13.0%
x1465
11.5%
40-541465
11.5%
millennials917
 
7.2%
25-39917
 
7.2%
baby829
 
6.5%
boomers829
 
6.5%
55-74829
 
6.5%
z196
 
1.5%
Other values (2)277
 
2.2%

Most occurring characters

ValueCountFrequency (%)
9304
 
12.1%
e6895
 
9.0%
a5234
 
6.8%
s5153
 
6.7%
r4398
 
5.7%
y4236
 
5.5%
54040
 
5.3%
43955
 
5.2%
n3576
 
4.7%
)3407
 
4.4%
Other values (21)26488
34.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter37555
49.0%
Decimal Number13628
 
17.8%
Space Separator9304
 
12.1%
Uppercase Letter5978
 
7.8%
Close Punctuation3407
 
4.4%
Open Punctuation3407
 
4.4%
Dash Punctuation3407
 
4.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e6895
18.4%
a5234
13.9%
s5153
13.7%
r4398
11.7%
y4236
11.3%
n3576
9.5%
l2751
 
7.3%
i1834
 
4.9%
o1658
 
4.4%
b829
 
2.2%
Other values (3)991
 
2.6%
Decimal Number
ValueCountFrequency (%)
54040
29.6%
43955
29.0%
01661
12.2%
21113
 
8.2%
9917
 
6.7%
3917
 
6.7%
7829
 
6.1%
1196
 
1.4%
Uppercase Letter
ValueCountFrequency (%)
G1661
27.8%
B1658
27.7%
X1465
24.5%
M917
15.3%
Z196
 
3.3%
I81
 
1.4%
Space Separator
ValueCountFrequency (%)
9304
100.0%
Close Punctuation
ValueCountFrequency (%)
)3407
100.0%
Open Punctuation
ValueCountFrequency (%)
(3407
100.0%
Dash Punctuation
ValueCountFrequency (%)
-3407
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin43533
56.8%
Common33153
43.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e6895
15.8%
a5234
12.0%
s5153
11.8%
r4398
10.1%
y4236
9.7%
n3576
8.2%
l2751
 
6.3%
i1834
 
4.2%
G1661
 
3.8%
B1658
 
3.8%
Other values (9)6137
14.1%
Common
ValueCountFrequency (%)
9304
28.1%
54040
12.2%
43955
11.9%
)3407
 
10.3%
(3407
 
10.3%
-3407
 
10.3%
01661
 
5.0%
21113
 
3.4%
9917
 
2.8%
3917
 
2.8%
Other values (2)1025
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII76686
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9304
 
12.1%
e6895
 
9.0%
a5234
 
6.8%
s5153
 
6.7%
r4398
 
5.7%
y4236
 
5.5%
54040
 
5.3%
43955
 
5.2%
n3576
 
4.7%
)3407
 
4.4%
Other values (21)26488
34.5%

address
Categorical

HIGH CARDINALITY
UNIFORM

Distinct3486
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size27.4 KiB
3 Talisman Place
 
2
3 Mariners Cove Terrace
 
2
060 Morning Avenue
 
1
0 Butterfield Junction
 
1
6505 Fieldstone Alley
 
1
Other values (3481)
3481 

Length

Max length29
Median length25
Mean length17.68262615
Min length10

Characters and Unicode

Total characters61677
Distinct characters60
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3484 ?
Unique (%)99.9%

Sample

1st row060 Morning Avenue
2nd row6 Meadow Vale Court
3rd row0 Holy Cross Court
4th row17979 Del Mar Point
5th row9 Oakridge Court

Common Values

ValueCountFrequency (%)
3 Talisman Place2
 
0.1%
3 Mariners Cove Terrace2
 
0.1%
060 Morning Avenue1
 
< 0.1%
0 Butterfield Junction1
 
< 0.1%
6505 Fieldstone Alley1
 
< 0.1%
5 1st Park1
 
< 0.1%
83 American Ash Drive1
 
< 0.1%
94 Twin Pines Trail1
 
< 0.1%
034 Eagan Avenue1
 
< 0.1%
2 Raven Way1
 
< 0.1%
Other values (3476)3476
99.7%

Length

2022-12-15T11:23:11.541290image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
crossing189
 
1.7%
pass184
 
1.7%
center183
 
1.7%
court180
 
1.7%
circle179
 
1.6%
trail179
 
1.6%
junction176
 
1.6%
place175
 
1.6%
street172
 
1.6%
lane172
 
1.6%
Other values (2505)9084
83.5%

Most occurring characters

ValueCountFrequency (%)
7385
 
12.0%
e4978
 
8.1%
a4087
 
6.6%
r3832
 
6.2%
n3090
 
5.0%
l2713
 
4.4%
o2622
 
4.3%
i2589
 
4.2%
t2147
 
3.5%
s1671
 
2.7%
Other values (50)26563
43.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter36457
59.1%
Decimal Number10500
 
17.0%
Space Separator7385
 
12.0%
Uppercase Letter7335
 
11.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e4978
13.7%
a4087
11.2%
r3832
10.5%
n3090
8.5%
l2713
 
7.4%
o2622
 
7.2%
i2589
 
7.1%
t2147
 
5.9%
s1671
 
4.6%
c1135
 
3.1%
Other values (16)7593
20.8%
Uppercase Letter
ValueCountFrequency (%)
P1127
15.4%
C1033
14.1%
S579
 
7.9%
A478
 
6.5%
T470
 
6.4%
M407
 
5.5%
L396
 
5.4%
H392
 
5.3%
D389
 
5.3%
R371
 
5.1%
Other values (13)1693
23.1%
Decimal Number
ValueCountFrequency (%)
31099
10.5%
21070
10.2%
01065
10.1%
81060
10.1%
11059
10.1%
41041
9.9%
51038
9.9%
61028
9.8%
91021
9.7%
71019
9.7%
Space Separator
ValueCountFrequency (%)
7385
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin43792
71.0%
Common17885
29.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e4978
 
11.4%
a4087
 
9.3%
r3832
 
8.8%
n3090
 
7.1%
l2713
 
6.2%
o2622
 
6.0%
i2589
 
5.9%
t2147
 
4.9%
s1671
 
3.8%
c1135
 
2.6%
Other values (39)14928
34.1%
Common
ValueCountFrequency (%)
7385
41.3%
31099
 
6.1%
21070
 
6.0%
01065
 
6.0%
81060
 
5.9%
11059
 
5.9%
41041
 
5.8%
51038
 
5.8%
61028
 
5.7%
91021
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII61677
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7385
 
12.0%
e4978
 
8.1%
a4087
 
6.6%
r3832
 
6.2%
n3090
 
5.0%
l2713
 
4.4%
o2622
 
4.3%
i2589
 
4.2%
t2147
 
3.5%
s1671
 
2.7%
Other values (50)26563
43.1%

postcode
Real number (ℝ≥0)

HIGH CORRELATION

Distinct835
Distinct (%)23.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2989.045872
Minimum2000
Maximum4883
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.4 KiB
2022-12-15T11:23:11.794864image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2000
5-th percentile2048
Q12200
median2768
Q33756.25
95-th percentile4551
Maximum4883
Range2883
Interquartile range (IQR)1556.25

Descriptive statistics

Standard deviation852.1480469
Coefficient of variation (CV)0.285090321
Kurtosis-0.9290874857
Mean2989.045872
Median Absolute Deviation (MAD)598
Skewness0.6222541559
Sum10425792
Variance726156.2939
MonotonicityNot monotonic
2022-12-15T11:23:12.033258image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
215328
 
0.8%
217028
 
0.8%
214527
 
0.8%
215526
 
0.7%
277024
 
0.7%
397722
 
0.6%
256020
 
0.6%
225020
 
0.6%
206520
 
0.6%
276319
 
0.5%
Other values (825)3254
93.3%
ValueCountFrequency (%)
20007
0.2%
20072
 
0.1%
20081
 
< 0.1%
20094
 
0.1%
201012
0.3%
20112
 
0.1%
20155
0.1%
20165
0.1%
20175
0.1%
20185
0.1%
ValueCountFrequency (%)
48831
 
< 0.1%
48792
 
0.1%
48783
 
0.1%
48771
 
< 0.1%
48733
 
0.1%
48709
0.3%
48695
0.1%
48683
 
0.1%
48601
 
< 0.1%
48255
0.1%

state
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.4 KiB
NSW
1866 
VIC
880 
QLD
742 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10464
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNSW
2nd rowNSW
3rd rowQLD
4th rowNSW
5th rowVIC

Common Values

ValueCountFrequency (%)
NSW1866
53.5%
VIC880
25.2%
QLD742
 
21.3%

Length

2022-12-15T11:23:12.227787image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-12-15T11:23:12.406854image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
nsw1866
53.5%
vic880
25.2%
qld742
 
21.3%

Most occurring characters

ValueCountFrequency (%)
N1866
17.8%
S1866
17.8%
W1866
17.8%
V880
8.4%
I880
8.4%
C880
8.4%
Q742
 
7.1%
L742
 
7.1%
D742
 
7.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter10464
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N1866
17.8%
S1866
17.8%
W1866
17.8%
V880
8.4%
I880
8.4%
C880
8.4%
Q742
 
7.1%
L742
 
7.1%
D742
 
7.1%

Most occurring scripts

ValueCountFrequency (%)
Latin10464
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N1866
17.8%
S1866
17.8%
W1866
17.8%
V880
8.4%
I880
8.4%
C880
8.4%
Q742
 
7.1%
L742
 
7.1%
D742
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII10464
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N1866
17.8%
S1866
17.8%
W1866
17.8%
V880
8.4%
I880
8.4%
C880
8.4%
Q742
 
7.1%
L742
 
7.1%
D742
 
7.1%

country
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.4 KiB
Australia
3488 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters31392
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAustralia
2nd rowAustralia
3rd rowAustralia
4th rowAustralia
5th rowAustralia

Common Values

ValueCountFrequency (%)
Australia3488
100.0%

Length

2022-12-15T11:23:12.538699image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-12-15T11:23:12.710709image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
australia3488
100.0%

Most occurring characters

ValueCountFrequency (%)
a6976
22.2%
A3488
11.1%
u3488
11.1%
s3488
11.1%
t3488
11.1%
r3488
11.1%
l3488
11.1%
i3488
11.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter27904
88.9%
Uppercase Letter3488
 
11.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a6976
25.0%
u3488
12.5%
s3488
12.5%
t3488
12.5%
r3488
12.5%
l3488
12.5%
i3488
12.5%
Uppercase Letter
ValueCountFrequency (%)
A3488
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin31392
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a6976
22.2%
A3488
11.1%
u3488
11.1%
s3488
11.1%
t3488
11.1%
r3488
11.1%
l3488
11.1%
i3488
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII31392
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a6976
22.2%
A3488
11.1%
u3488
11.1%
s3488
11.1%
t3488
11.1%
r3488
11.1%
l3488
11.1%
i3488
11.1%

property_valuation
Real number (ℝ≥0)

HIGH CORRELATION

Distinct12
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.515768349
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.4 KiB
2022-12-15T11:23:12.826028image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q16
median8
Q310
95-th percentile11
Maximum12
Range11
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.822801011
Coefficient of variation (CV)0.3755838232
Kurtosis-0.3251201083
Mean7.515768349
Median Absolute Deviation (MAD)2
Skewness-0.6406124299
Sum26215
Variance7.968205547
MonotonicityNot monotonic
2022-12-15T11:23:12.989704image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
9573
16.4%
8572
16.4%
10498
14.3%
7423
12.1%
11244
7.0%
6207
 
5.9%
5196
 
5.6%
4187
 
5.4%
12169
 
4.8%
3160
 
4.6%
Other values (2)259
7.4%
ValueCountFrequency (%)
1139
 
4.0%
2120
 
3.4%
3160
 
4.6%
4187
 
5.4%
5196
 
5.6%
6207
 
5.9%
7423
12.1%
8572
16.4%
9573
16.4%
10498
14.3%
ValueCountFrequency (%)
12169
 
4.8%
11244
7.0%
10498
14.3%
9573
16.4%
8572
16.4%
7423
12.1%
6207
 
5.9%
5196
 
5.6%
4187
 
5.4%
3160
 
4.6%

Frequency
Real number (ℝ)

HIGH CORRELATION

Distinct108
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.12643349
Minimum-11
Maximum97
Zeros30
Zeros (%)0.9%
Negative173
Negative (%)5.0%
Memory size27.4 KiB
2022-12-15T11:23:13.178203image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-11
5-th percentile0
Q118
median42
Q368
95-th percentile89
Maximum97
Range108
Interquartile range (IQR)50

Descriptive statistics

Standard deviation28.70681501
Coefficient of variation (CV)0.6656431495
Kurtosis-1.16093077
Mean43.12643349
Median Absolute Deviation (MAD)24
Skewness0.05723707919
Sum150425
Variance824.0812282
MonotonicityNot monotonic
2022-12-15T11:23:13.384763image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6551
 
1.5%
3351
 
1.5%
6148
 
1.4%
1848
 
1.4%
6946
 
1.3%
6246
 
1.3%
4146
 
1.3%
1145
 
1.3%
844
 
1.3%
3044
 
1.3%
Other values (98)3019
86.6%
ValueCountFrequency (%)
-112
 
0.1%
-96
 
0.2%
-810
 
0.3%
-713
0.4%
-617
0.5%
-520
0.6%
-420
0.6%
-332
0.9%
-225
0.7%
-128
0.8%
ValueCountFrequency (%)
972
 
0.1%
965
 
0.1%
9511
 
0.3%
9422
0.6%
9328
0.8%
9236
1.0%
9144
1.3%
9021
0.6%
8932
0.9%
8829
0.8%

profit
Real number (ℝ≥0)

HIGH CORRELATION

Distinct3391
Distinct (%)97.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3127.561411
Minimum15.1
Maximum11668.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.4 KiB
2022-12-15T11:23:13.595927image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum15.1
5-th percentile641.145
Q11841.175
median2859.75
Q34179.875
95-th percentile6365.295
Maximum11668.9
Range11653.8
Interquartile range (IQR)2338.7

Descriptive statistics

Standard deviation1770.695072
Coefficient of variation (CV)0.5661583707
Kurtosis0.7957856964
Mean3127.561411
Median Absolute Deviation (MAD)1131.2
Skewness0.778913283
Sum10908934.2
Variance3135361.039
MonotonicityNot monotonic
2022-12-15T11:23:13.800581image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2795.83
 
0.1%
2073.82
 
0.1%
3660.52
 
0.1%
2500.32
 
0.1%
23442
 
0.1%
3081.12
 
0.1%
4965.72
 
0.1%
1924.22
 
0.1%
299.32
 
0.1%
4557.62
 
0.1%
Other values (3381)3467
99.4%
ValueCountFrequency (%)
15.11
< 0.1%
17.91
< 0.1%
35.71
< 0.1%
41.12
0.1%
50.21
< 0.1%
50.71
< 0.1%
57.71
< 0.1%
63.81
< 0.1%
64.51
< 0.1%
75.51
< 0.1%
ValueCountFrequency (%)
11668.91
< 0.1%
11222.61
< 0.1%
10787.61
< 0.1%
10640.31
< 0.1%
10497.81
< 0.1%
104221
< 0.1%
10341.61
< 0.1%
10028.81
< 0.1%
9739.51
< 0.1%
9695.61
< 0.1%

Recency
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct280
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.36353211
Minimum0
Maximum353
Zeros47
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size27.4 KiB
2022-12-15T11:23:14.017775image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q117
median44
Q386
95-th percentile181
Maximum353
Range353
Interquartile range (IQR)69

Descriptive statistics

Standard deviation58.41284552
Coefficient of variation (CV)0.9519146554
Kurtosis2.720416376
Mean61.36353211
Median Absolute Deviation (MAD)31
Skewness1.570525691
Sum214036
Variance3412.060522
MonotonicityNot monotonic
2022-12-15T11:23:14.237671image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1160
 
1.7%
1457
 
1.6%
255
 
1.6%
1253
 
1.5%
151
 
1.5%
451
 
1.5%
551
 
1.5%
2451
 
1.5%
850
 
1.4%
1749
 
1.4%
Other values (270)2960
84.9%
ValueCountFrequency (%)
047
1.3%
151
1.5%
255
1.6%
349
1.4%
451
1.5%
551
1.5%
642
1.2%
742
1.2%
850
1.4%
947
1.3%
ValueCountFrequency (%)
3531
< 0.1%
3381
< 0.1%
3331
< 0.1%
3291
< 0.1%
3281
< 0.1%
3251
< 0.1%
3211
< 0.1%
3151
< 0.1%
3121
< 0.1%
3081
< 0.1%

R_rank
Real number (ℝ≥0)

HIGH CORRELATION

Distinct280
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1745.308343
Minimum1
Maximum3466
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.4 KiB
2022-12-15T11:23:14.479704image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile176
Q1869
median1744
Q32635
95-th percentile3312
Maximum3466
Range3465
Interquartile range (IQR)1766

Descriptive statistics

Standard deviation1007.26688
Coefficient of variation (CV)0.5771283247
Kurtosis-1.199998587
Mean1745.308343
Median Absolute Deviation (MAD)875
Skewness-0.0007882302217
Sum6087635.5
Variance1014586.568
MonotonicityNot monotonic
2022-12-15T11:23:14.692488image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2927.560
 
1.7%
277157
 
1.6%
336455
 
1.6%
287153
 
1.5%
341751
 
1.5%
326251
 
1.5%
321151
 
1.5%
236951
 
1.5%
3076.550
 
1.4%
263549
 
1.4%
Other values (270)2960
84.9%
ValueCountFrequency (%)
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
101
< 0.1%
ValueCountFrequency (%)
346647
1.3%
341751
1.5%
336455
1.6%
331249
1.4%
326251
1.5%
321151
1.5%
3164.542
1.2%
3122.542
1.2%
3076.550
1.4%
302847
1.3%

F_rank
Real number (ℝ≥0)

HIGH CORRELATION

Distinct108
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1744.912701
Minimum1.5
Maximum3488.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.4 KiB
2022-12-15T11:23:14.909376image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile188.5
Q1870.5
median1744.5
Q32633
95-th percentile3304.5
Maximum3488.5
Range3487
Interquartile range (IQR)1762.5

Descriptive statistics

Standard deviation1007.409204
Coefficient of variation (CV)0.5773407483
Kurtosis-1.200605693
Mean1744.912701
Median Absolute Deviation (MAD)874
Skewness0.0002645725479
Sum6086255.5
Variance1014873.305
MonotonicityNot monotonic
2022-12-15T11:23:15.105944image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
253551
 
1.5%
140651
 
1.5%
2363.548
 
1.4%
870.548
 
1.4%
2673.546
 
1.3%
2410.546
 
1.3%
1705.546
 
1.3%
59645
 
1.3%
484.544
 
1.3%
1286.544
 
1.3%
Other values (98)3019
86.6%
ValueCountFrequency (%)
1.52
 
0.1%
5.56
 
0.2%
13.510
 
0.3%
2513
0.4%
4017
0.5%
58.520
0.6%
78.520
0.6%
104.532
0.9%
13325
0.7%
159.528
0.8%
ValueCountFrequency (%)
3488.52
 
0.1%
34855
 
0.1%
347711
 
0.3%
3460.522
0.6%
3435.528
0.8%
3403.536
1.0%
3363.544
1.3%
333121
0.6%
3304.532
0.9%
327429
0.8%

M_rank
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM

Distinct3470
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1744.664851
Minimum1
Maximum3489
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.4 KiB
2022-12-15T11:23:15.322695image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile175.35
Q1872.75
median1744.5
Q32616.25
95-th percentile3314.65
Maximum3489
Range3488
Interquartile range (IQR)1743.5

Descriptive statistics

Standard deviation1007.28173
Coefficient of variation (CV)0.577349701
Kurtosis-1.199644951
Mean1744.664851
Median Absolute Deviation (MAD)872
Skewness0.0005502358331
Sum6085391
Variance1014616.484
MonotonicityNot monotonic
2022-12-15T11:23:15.525572image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52.52
 
0.1%
44.52
 
0.1%
4.52
 
0.1%
196.52
 
0.1%
391.52
 
0.1%
95.52
 
0.1%
126.52
 
0.1%
80.52
 
0.1%
75.52
 
0.1%
698.52
 
0.1%
Other values (3460)3468
99.4%
ValueCountFrequency (%)
11
< 0.1%
21
< 0.1%
31
< 0.1%
4.52
0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
101
< 0.1%
111
< 0.1%
ValueCountFrequency (%)
34891
< 0.1%
34881
< 0.1%
34871
< 0.1%
34861
< 0.1%
34851
< 0.1%
34841
< 0.1%
34831
< 0.1%
34821
< 0.1%
34811
< 0.1%
34801
< 0.1%

R_rank_norm
Real number (ℝ≥0)

HIGH CORRELATION

Distinct228
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.35699541
Minimum0
Maximum100
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size27.4 KiB
2022-12-15T11:23:15.747967image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.1
Q125.1
median50.3
Q376
95-th percentile95.6
Maximum100
Range100
Interquartile range (IQR)50.9

Descriptive statistics

Standard deviation29.06136437
Coefficient of variation (CV)0.5771067978
Kurtosis-1.199520715
Mean50.35699541
Median Absolute Deviation (MAD)25.2
Skewness-0.0007491943219
Sum175645.2
Variance844.5628991
MonotonicityNot monotonic
2022-12-15T11:23:15.984346image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
84.560
 
1.7%
79.957
 
1.6%
97.155
 
1.6%
82.853
 
1.5%
94.151
 
1.5%
68.351
 
1.5%
92.651
 
1.5%
98.651
 
1.5%
88.850
 
1.4%
7649
 
1.4%
Other values (218)2960
84.9%
ValueCountFrequency (%)
01
 
< 0.1%
0.14
0.1%
0.23
0.1%
0.34
0.1%
0.44
0.1%
0.53
0.1%
0.63
0.1%
0.73
0.1%
0.84
0.1%
0.93
0.1%
ValueCountFrequency (%)
10047
1.3%
98.651
1.5%
97.155
1.6%
95.649
1.4%
94.151
1.5%
92.651
1.5%
91.342
1.2%
90.142
1.2%
88.850
1.4%
87.447
1.3%

F_rank_norm
Real number (ℝ≥0)

HIGH CORRELATION

Distinct108
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.02462729
Minimum0
Maximum100
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size27.4 KiB
2022-12-15T11:23:16.195422image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.4
Q125
median50
Q375.5
95-th percentile94.7
Maximum100
Range100
Interquartile range (IQR)50.5

Descriptive statistics

Standard deviation28.8760021
Coefficient of variation (CV)0.5772357269
Kurtosis-1.200430976
Mean50.02462729
Median Absolute Deviation (MAD)25
Skewness3.903813219 × 10-5
Sum174485.9
Variance833.8234971
MonotonicityNot monotonic
2022-12-15T11:23:16.404603image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
72.751
 
1.5%
40.351
 
1.5%
67.848
 
1.4%
2548
 
1.4%
76.646
 
1.3%
69.146
 
1.3%
48.946
 
1.3%
17.145
 
1.3%
13.944
 
1.3%
36.944
 
1.3%
Other values (98)3019
86.6%
ValueCountFrequency (%)
02
 
0.1%
0.26
 
0.2%
0.410
 
0.3%
0.713
0.4%
1.117
0.5%
1.720
0.6%
2.320
0.6%
332
0.9%
3.825
0.7%
4.628
0.8%
ValueCountFrequency (%)
1002
 
0.1%
99.95
 
0.1%
99.711
 
0.3%
99.222
0.6%
98.528
0.8%
97.636
1.0%
96.444
1.3%
95.521
0.6%
94.732
0.9%
93.929
0.8%

M_rank_norm
Real number (ℝ≥0)

HIGH CORRELATION

Distinct108
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.01356078
Minimum0
Maximum100
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size27.4 KiB
2022-12-15T11:23:16.619640image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.4
Q124.9
median50
Q375.5
95-th percentile94.7
Maximum100
Range100
Interquartile range (IQR)50.6

Descriptive statistics

Standard deviation28.87180962
Coefficient of variation (CV)0.5772796251
Kurtosis-1.200440031
Mean50.01356078
Median Absolute Deviation (MAD)25.1
Skewness4.213769777 × 10-5
Sum174447.3
Variance833.5813905
MonotonicityNot monotonic
2022-12-15T11:23:16.819996image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
72.751
 
1.5%
40.351
 
1.5%
67.748
 
1.4%
24.948
 
1.4%
76.646
 
1.3%
69.146
 
1.3%
48.946
 
1.3%
17.145
 
1.3%
13.944
 
1.3%
36.944
 
1.3%
Other values (98)3019
86.6%
ValueCountFrequency (%)
02
 
0.1%
0.26
 
0.2%
0.410
 
0.3%
0.713
0.4%
1.117
0.5%
1.720
0.6%
2.220
0.6%
332
0.9%
3.825
0.7%
4.628
0.8%
ValueCountFrequency (%)
1002
 
0.1%
99.95
 
0.1%
99.711
 
0.3%
99.222
0.6%
98.528
0.8%
97.536
1.0%
96.444
1.3%
95.521
0.6%
94.732
0.9%
93.829
0.8%

RFM_Score
Real number (ℝ≥0)

HIGH CORRELATION

Distinct49
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.506450688
Minimum0.1
Maximum4.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.4 KiB
2022-12-15T11:23:17.035081image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.8
Q11.7
median2.5
Q33.3
95-th percentile4.2
Maximum4.9
Range4.8
Interquartile range (IQR)1.6

Descriptive statistics

Standard deviation1.069541398
Coefficient of variation (CV)0.4267155156
Kurtosis-0.8591577538
Mean2.506450688
Median Absolute Deviation (MAD)0.8
Skewness-0.004783144915
Sum8742.5
Variance1.143918801
MonotonicityNot monotonic
2022-12-15T11:23:17.240386image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
3.1123
 
3.5%
2.1114
 
3.3%
3.3114
 
3.3%
2.9111
 
3.2%
2.6111
 
3.2%
3.5111
 
3.2%
2.4110
 
3.2%
1.8107
 
3.1%
3106
 
3.0%
1.6104
 
3.0%
Other values (39)2377
68.1%
ValueCountFrequency (%)
0.14
 
0.1%
0.26
 
0.2%
0.316
 
0.5%
0.425
0.7%
0.528
0.8%
0.637
1.1%
0.744
1.3%
0.857
1.6%
0.959
1.7%
160
1.7%
ValueCountFrequency (%)
4.96
 
0.2%
4.815
 
0.4%
4.719
 
0.5%
4.614
 
0.4%
4.529
0.8%
4.442
1.2%
4.348
1.4%
4.250
1.4%
4.153
1.5%
465
1.9%

Customer_segment
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.4 KiB
Low Value
1425 
Medium Value
918 
Inactive
869 
High value
222 
Top
 
54

Length

Max length12
Median length10
Mean length9.511181193
Min length3

Characters and Unicode

Total characters33175
Distinct characters23
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHigh value
2nd rowMedium Value
3rd rowInactive
4th rowMedium Value
5th rowLow Value

Common Values

ValueCountFrequency (%)
Low Value1425
40.9%
Medium Value918
26.3%
Inactive869
24.9%
High value222
 
6.4%
Top54
 
1.5%

Length

2022-12-15T11:23:17.441350image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-12-15T11:23:17.628928image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
value2565
42.4%
low1425
23.5%
medium918
 
15.2%
inactive869
 
14.4%
high222
 
3.7%
top54
 
0.9%

Most occurring characters

ValueCountFrequency (%)
e4352
13.1%
u3483
10.5%
a3434
10.4%
2565
 
7.7%
l2565
 
7.7%
V2343
 
7.1%
i2009
 
6.1%
o1479
 
4.5%
L1425
 
4.3%
w1425
 
4.3%
Other values (13)8095
24.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter24779
74.7%
Uppercase Letter5831
 
17.6%
Space Separator2565
 
7.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e4352
17.6%
u3483
14.1%
a3434
13.9%
l2565
10.4%
i2009
8.1%
o1479
 
6.0%
w1425
 
5.8%
v1091
 
4.4%
d918
 
3.7%
m918
 
3.7%
Other values (6)3105
12.5%
Uppercase Letter
ValueCountFrequency (%)
V2343
40.2%
L1425
24.4%
M918
 
15.7%
I869
 
14.9%
H222
 
3.8%
T54
 
0.9%
Space Separator
ValueCountFrequency (%)
2565
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin30610
92.3%
Common2565
 
7.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e4352
14.2%
u3483
11.4%
a3434
11.2%
l2565
 
8.4%
V2343
 
7.7%
i2009
 
6.6%
o1479
 
4.8%
L1425
 
4.7%
w1425
 
4.7%
v1091
 
3.6%
Other values (12)7004
22.9%
Common
ValueCountFrequency (%)
2565
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII33175
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e4352
13.1%
u3483
10.5%
a3434
10.4%
2565
 
7.7%
l2565
 
7.7%
V2343
 
7.1%
i2009
 
6.1%
o1479
 
4.5%
L1425
 
4.3%
w1425
 
4.3%
Other values (13)8095
24.4%

Interactions

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2022-12-15T11:22:33.679520image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:36.731214image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:40.612939image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:43.894673image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:47.078310image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:50.207509image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:53.414834image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:56.524021image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:59.520146image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:23:02.808316image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:11.416842image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:15.483194image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:21.518288image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:24.628736image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:27.733284image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:30.784193image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:33.826129image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:36.929675image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:40.806298image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:44.087793image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:47.232958image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:50.399424image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:53.601341image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:56.715722image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:59.709929image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:23:02.994509image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:11.610749image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:15.680869image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:21.703230image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:24.820301image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:27.923671image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:30.939879image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:34.022996image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:37.948990image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:41.080681image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:44.281066image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:47.422107image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:50.597715image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:53.790731image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:56.915594image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:59.893314image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:23:03.181095image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:11.818650image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:15.878504image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:21.916174image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:25.012629image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:28.115753image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:31.130702image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:34.216416image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:38.098597image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:41.285983image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:44.439042image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:47.619734image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:50.790927image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:53.992058image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:57.104579image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:23:00.081290image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:23:03.327721image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:12.037841image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:16.029734image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:22.119404image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:25.199408image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:28.302580image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:31.312837image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:34.397853image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:38.285224image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:41.430726image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:44.629559image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:47.810719image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:50.992113image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:54.179276image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:57.280687image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:23:00.227585image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:23:03.508204image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:12.223751image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:16.217546image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:22.301017image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:25.384114image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:28.491208image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:31.498770image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:34.589440image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:38.436535image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:41.617191image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:44.821163image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:47.997925image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:51.195554image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:54.327460image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:57.425562image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:23:00.407931image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:23:03.687711image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:12.411196image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:16.403302image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:22.478998image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:25.533572image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:28.635605image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:31.645486image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:34.731027image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:38.620692image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:41.821719image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:45.006335image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:48.182272image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:51.383139image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:54.517364image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:22:57.605993image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-15T11:23:00.586937image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-12-15T11:23:17.828046image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-12-15T11:23:18.126889image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-12-15T11:23:18.422998image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-12-15T11:23:18.712799image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-12-15T11:23:18.982167image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-12-15T11:23:19.219671image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-12-15T11:23:04.005419image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-12-15T11:23:05.036687image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-12-15T11:23:05.319794image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-12-15T11:23:05.495533image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

df_indexcustomer_idgenderpast_3_years_bike_related_purchasesDOBjob_titlejob_industry_categorywealth_segmentdeceased_indicatorowns_cartenureageage_groupaddresspostcodestatecountryproperty_valuationFrequencyprofitRecencyR_rankF_rankM_rankR_rank_normF_rank_normM_rank_normRFM_ScoreCustomer_segment
001Female931953-10-12Executive SecretaryHealthMass Customer0111.069.0Baby Boomers (55-74 years)060 Morning Avenue2016NSWAustralia10823018.173122.53097.51857.090.188.888.84.5High value
112Male811980-12-16Administrative OfficerFinancial ServicesMass Customer0116.042.0Gen X (40-54 years)6 Meadow Vale Court2153NSWAustralia10782226.3128457.52973.01187.013.285.285.23.1Medium Value
224Male331961-10-03UnknownITMass Customer007.061.0Baby Boomers (55-74 years)0 Holy Cross Court4211QLDAustralia931220.6195137.51327.055.04.038.038.01.3Inactive
335Female561977-05-13Senior EditorUnknownAffluent Customer018.045.0Gen X (40-54 years)17979 Del Mar Point2448NSWAustralia4502394.9162684.02013.01336.077.457.757.73.2Medium Value
446Male351966-09-16UnknownRetailHigh Net Worth0113.056.0Baby Boomers (55-74 years)9 Oakridge Court3216VICAustralia9303946.6641263.51286.52491.036.536.936.91.8Low Value
557Female61976-02-23UnknownFinancial ServicesAffluent Customer0111.046.0Gen X (40-54 years)4 Delaware Trail2210NSWAustralia93220.125350.0302.554.01.48.78.70.3Inactive
668Male311962-03-30Media Manager IUnknownMass Customer007.060.0Baby Boomers (55-74 years)49 Londonderry Lane2650NSWAustralia4217066.9222440.5974.03395.070.427.927.92.1Low Value
779Female971973-03-10Business Systems Development AnalystArgicultureAffluent Customer018.049.0Gen X (40-54 years)97736 7th Trail2023NSWAustralia12912353.1781003.53363.51309.029.096.496.43.7Medium Value
8811Male991954-04-30UnknownPropertyMass Customer009.068.0Baby Boomers (55-74 years)93405 Ludington Park3044VICAustralia8933638.8461686.03435.52299.048.698.598.54.1High value
9912Male581994-07-21Nuclear Power EngineerManufacturingMass Customer008.028.0Millennials (25-39 years)44339 Golden Leaf Alley4557QLDAustralia4513540.0671205.02049.52226.034.858.858.72.5Low Value

Last rows

df_indexcustomer_idgenderpast_3_years_bike_related_purchasesDOBjob_titlejob_industry_categorywealth_segmentdeceased_indicatorowns_cartenureageage_groupaddresspostcodestatecountryproperty_valuationFrequencyprofitRecencyR_rankF_rankM_rankR_rank_normF_rank_normM_rank_normRFM_ScoreCustomer_segment
347834793491Female691976-04-03Business Systems Development AnalystFinancial ServicesAffluent Customer0010.046.0Gen X (40-54 years)82 Dahle Crossing3195VICAustralia10651430.3189150.52535.0583.04.372.772.72.5Low Value
347934803492Male831966-01-27Civil EngineerManufacturingMass Customer0019.056.0Baby Boomers (55-74 years)2986 Holmberg Circle3021VICAustralia9802193.880969.53034.01155.028.087.087.03.4Medium Value
348034813493Male301964-02-29Research Assistant IHealthHigh Net Worth0018.058.0Baby Boomers (55-74 years)3 Monument Crossing2090NSWAustralia10243728.993777.51072.52362.022.430.730.71.4Inactive
348134823494Male721998-12-24Account Representative IVArgicultureHigh Net Worth001.024.0Gen Z (10-24 years)35 Chive Alley2033NSWAustralia10682755.143262.02633.01656.094.175.575.54.1High value
348234833495Female571987-07-12Programmer IIIFinancial ServicesHigh Net Worth008.035.0Millennials (25-39 years)1 Dayton Park2767NSWAustralia9503847.6132822.02013.02429.081.457.757.73.3Medium Value
348334843496Male991986-04-25EditorManufacturingMass Customer0119.036.0Millennials (25-39 years)2565 Caliangt Point2171NSWAustralia9952045.825646.03477.01010.01.399.799.73.4Medium Value
348434853497Female731986-05-03Administrative Assistant IVManufacturingAffluent Customer0118.036.0Millennials (25-39 years)96 Delladonna Trail3976VICAustralia5701648.3521544.02715.0719.044.577.877.83.3Medium Value
348534863498Female281995-11-02UnknownManufacturingMass Customer005.027.0Millennials (25-39 years)3 Nova Point3012VICAustralia4223147.3127462.51007.51943.013.328.928.91.2Inactive
348634873499Male291979-06-17UnknownManufacturingMass Customer017.043.0Gen X (40-54 years)310 Stephen Terrace4073QLDAustralia9224955.2511566.51007.52968.045.228.928.91.7Low Value
348734883500Female711967-07-21UnknownEntertainmentAffluent Customer0017.055.0Baby Boomers (55-74 years)9491 Green Ridge Terrace2100NSWAustralia10651785.9144338.02535.0822.09.872.772.72.6Low Value